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RNA Editing

RNA Editing

RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...

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Predictive Immune Modeling of Solid Tumors

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Integration of RNA Editing into Multiomics Machine Learning Models for Predicting Drug Responses in Breast Cancer

Yanara A Bernal^1,2, Alejandro Blanco¹, Karen Oróstica³

¹Centro de Genética y Genómica, Instituto de Ciencias e Innovación en Medicina, Facultad de Medicina Clínica Alemana Universidad del Desarrollo, Santiago 7550000, Chile.

|March 28, 2026

Summary

RNA editing data enhances artificial intelligence models for predicting breast cancer drug response. Integrating epitranscriptomic features alongside genomics and transcriptomics improves predictive accuracy in precision oncology.

Keywords:

RNA editing breast cancer drug response machine learning multi-omics precision medicine

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Area of Science:

Computational biology
Genomics
Precision medicine

Background:

Multi-omics data integration with AI advances precision medicine.
Clinical application of AI models is limited by complexity.
This study integrates DNA mutation, RNA expression, and RNA editing for breast cancer drug response prediction.

Purpose of the Study:

To develop a predictive model for breast cancer drug response using integrated multi-omics data.
To evaluate the contribution of RNA editing to predictive model performance.
To explore the potential of epitranscriptomic features in precision oncology.

Main Methods:

Analysis of 104 breast cancer patients from NCT02022202.
Integration of clinical, DNA mutation, RNA expression, and RNA editing data.
Machine learning models (GLM, RF, SVM) with LASSO regularization for feature selection and prediction, evaluated using F1-score.

Main Results:

Characterization of a cohort with 69 non-responders and 35 responders.
RNA editing data frequently maintained or improved predictive performance when added to models.
Paired analyses showed a statistically significant increase in F1-score with the inclusion of RNA editing.

Conclusions:

RNA editing provides a complementary molecular layer for multi-omics models.
Epitranscriptomic features can enhance therapy response prediction in breast cancer.
Further investigation of RNA editing in precision oncology is warranted.